Recently, deep learning based methods have shown superior performance in many computer vision tasks. These methods have been successfully adapted for the MRI reconstruction problem and have shown promising results. The deep learning based methods [1]} for MRI reconstruction can be broadly grouped into two : 1) k-space to image domain: the fully sampled image is obtained from zero-filled k-space. Examples include AUTOMAP and ADMM-Net. 2) image to image domain: the fully sampled (FS) image is obtained from the zero-filled (ZF) image. Our focus will be on the models of the latter kind. The work by Wang et al. [2]} was the first to use convolutional neural networks to learn the mapping between ZF and FS images. Generative Adversarial Networks (GAN) [3]} have shown promising results in many ill-posed inverse problems such as inpainting, super-resolution and denoising when compared to other deep learning based methods. The MRI reconstruction problem, having a similar problem formulation, has been approached with GANs and have shown encouraging results. The main focus of our paper is thus the application of GANs for the MRI reconstruction problem.
